UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis (ICML 2024)

This is the origin Pytorch implementation of “UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis (ICML 2024)”

Workflow

UP2ME is a general-purpose framework for Multivariate Time Series Analysis. It conducts taskagnostic pre-training when downstream tasks are unspecified. Once the task and setting (e.g. forecasting length) are determined, it gives sensible solutions with frozen pre-trained parameters. Further accuracy is achieved through multivariate fine-tuning.

Keypoints

Overview of UP2ME framework. Left: Univariate Pre-training. Univariate instances are generated using variable window length and channel decoupling. Generated instances are fed into the encoder and decoder for Masked AutoEnncoder (MAE) pre-training. Formulating downstream tasks as specific mask-reconstruction problems, UP2ME can give sensible solutions without parameter modification (right part without TC layers). Right: Multivariate Fine-tuning (forecasting in this example). The pre-trained frozen encoder encodes a multivariate series into latent tokens. The tokens are used to construct a dependency graph among channels. Learnable Temporal-Channel (TC) layers which take constructed graph as input, are inserted before the frozen decoder for fine-tuning.

Univariate Pretraining

1. Variable Window Length: To meet the uncertain requirements for window length, for each pre-training step, we randomly sample a window length $L$ then generate a batch of instances with this length.

2. Channel Decoupling: To generate an instance of length $L$, instead of extracting a multivariate sub-series, we independently sample a time span and a channel index to generate a univariate sub-series.

3. Immediate Reaction Mode: After pre-training, UP2ME can perform immediate forecasting, anomaly detection and imputation with frozen parameters by formulating them into specific mask-reconstruction problems:

   3.1. Forecasting: Past series are viewed as unmasked patches and future series are viewed as masked patches to reconstruct.

   3.2. Imputation: Fully-observed patches are viewed as unmasked patches and patches containing at least one missing point are viewed as masked.

   3.3 Anomaly Detection: Iteratively mask each patch and use other unmasked patches to reconstruct it. Difference between reconstructed series and original series is used as the anomaly score.

Multivariate Fine-tuning

1. Sparse Dependency Graph Construction: A sparse dependency graph is constructed using representations output by the pre-trained encoder to guide cross-channel dependency capturing.

2. Temporal-Channel (TC) layer: We freeze parameters of the pre-trained encoder and decoder while inserting learnable Temporal-Channel (TC) layers between them to capture cross-channel dependency and adjust temporal dependency. With few inductive biases, our TC layer contains a standard Transformer layer and a standard Graph Transformer layer. The Graph Transformer layer takes the constructed dependency graph as input.

Reproducibility

1. Install requirements by:

   pip install -r requirements.txt
   

2. Download the datasets from UP2ME-datasets and unzip it into the folder datasets in the root folder. The struture should be like:

   datasets
   ├── ETT
   │   └── ETTm1.csv
   ├── same for csv format datasets: weather, ECL(Electricity) and traffic
   ├── SMD
   │   ├── SMD_train.npy
   │   ├── SMD_test.npy
   │   └── SMD_test_label.npy
   └── same for npy format datasets: PSM, SWaT and NIPS_Water(GECCO)
   

3. We have already put the pre-trained model for each dataset in ./pretrain-library. To get forecasting results on ETTm1, run:

   bash scripts/forecast_scripts/ETTm1.sh
   

   the immediate reaction(UP2ME(IR)) and fine-tuning(UP2ME(FT)) modes will be tested on 4 different forecasting lengths (96, 192, 336, 720) and results will be saved in a new folder ./forecast_results.

4. To reproduce results for all 3 tasks on all 8 datasets, run other scripts in ./scripts.

Citation

If you find this repository useful in your research, please cite:

@inproceedings{zhang2024upme,
title={{UP}2{ME}:  Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis},
author={Yunhao Zhang and Minghao Liu and Shengyang Zhou and Junchi Yan},
booktitle={International Conference on Machine Learning (ICML)},
year={2024}
}

Acknowledgement

We appreciate the following works for their valuable code and data:

https://github.com/thuml/Time-Series-Library

https://github.com/DAMO-DI-ML/KDD2023-DCdetector

https://github.com/zezhishao/STEP

https://github.com/facebookresearch/mae